We also evaluate how our pre-trained model performs on the academic dataset, e.g., FashionGen [1]}.
We strictly follow  [2]} constructing its image-text retrieval task.
In its text-to-image retrieval, the model is required to pick the matched image from 101 images given a text.
In the 101 images, one is positively paired with the text and the other 100 are randomly paired but sharing the same sub-category as the positive, increasing the difficulty.
The same setting is for its image-to-text retrieval.
We fine-tune our smallest ResNet50-based 6-layer CommerceMM (L9 in Table REF ) on the dataset with contrastive learning.
In Table. REF , we compare our model with the state-of-art commerce-domain pre-trained models [3]}, [2]}.
We found even our smallest model already outperforms [3]}, [2]} with a clear margin, indicating CommerceMM's superior transferability.
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